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opts.py
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opts.py
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import argparse
import os
from utils.helper import str2bool
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--id', type=str, default='test',
help='an id identifying this run/job. used in cross-val and appended when writing progress files')
parser.add_argument('--gpus', type=str, default='0', help='set CUDA_VISIBLE_DEVICES')
# Data input settings
parser.add_argument('--input_json', type=str, default='data/cocotalk_final.json',
help='path to the json file containing additional info and vocab')
parser.add_argument('--input_fc_dir', type=str, default='data/cocobu_fc',
help='path to the directory containing the preprocessed fc feats')
parser.add_argument('--input_att_dir', type=str, default='data/cocobu_att',
help='path to the directory containing the preprocessed att feats')
parser.add_argument('--input_label_h5', type=str, default='data/cocotalk_label.h5',
help='path to the h5file containing the preprocessed dataset')
parser.add_argument('--train_only', type=int, default=0,
help='if true then use 80k, else use 110k')
parser.add_argument('--cached_tokens', type=str, default='coco-train-idxs',
help='Cached token file for calculating cider score during self critical training.')
parser.add_argument('--loader_num_workers', type=int, default=4,
help='num of processes to use for BlobFetcher')
# load model and settings
parser.add_argument('--resume_from', type=str, default=None,
help="continuing training from this experiment id")
parser.add_argument('--resume_from_best', type=str2bool, default=False,
help='resume from best model, True: use best_model.pth, False: use model.pth')
parser.add_argument('--load_best_score', type=int, default=1,
help='Do we load previous best score when resuming training.')
# Model settings
parser.add_argument('--caption_model', type=str, default="vrgmodel",
help='model type: [vrgmodel]')
parser.add_argument('--rnn_size', type=int, default=1000,
help='size of the rnn in number of hidden nodes in each layer')
parser.add_argument('--num_layers', type=int, default=1,
help='number of layers in the RNN')
parser.add_argument('--rnn_type', type=str, default='lstm',
help='rnn, gru, or lstm')
parser.add_argument('--input_encoding_size', type=int, default=1000,
help='the encoding size of each token in the vocabulary, and the image.')
parser.add_argument('--att_hid_size', type=int, default=512,
help='the hidden size of the attention MLP; only useful in show_attend_tell; 0 if not using hidden layer')
parser.add_argument('--fc_feat_size', type=int, default=2048,
help='2048 for resnet, 4096 for vgg')
parser.add_argument('--att_feat_size', type=int, default=2048,
help='2048 for resnet, 512 for vgg')
parser.add_argument('--logit_layers', type=int, default=1,
help='number of layers in the RNN')
# feature manipulation
parser.add_argument('--norm_att_feat', type=int, default=0,
help='If normalize attention features')
parser.add_argument('--use_box', type=str2bool, default=False,
help='If use box features')
parser.add_argument('--norm_box_feat', type=int, default=0,
help='If use box, do we normalize box feature')
parser.add_argument('--use_bn', type=int, default=0,
help='If 1, then do batch_normalization first in att_embed, if 2 then do bn both in the beginning and the end of att_embed')
# Optimization: General
parser.add_argument('--max_epochs', type=int, default=25,
help='number of epochs')
parser.add_argument('--batch_size', type=int, default=128,
help='minibatch size')
parser.add_argument('--grad_clip', type=float, default=0.1, #5.,
help='clip gradients at this value')
parser.add_argument('--drop_prob_lm', type=float, default=0.5,
help='strength of dropout in the Language Model RNN')
parser.add_argument('--self_critical_after', type=int, default=-1,
help='After what epoch do we start finetuning the CNN? (-1 = disable; never finetune, 0 = finetune from start)')
parser.add_argument('--seq_per_img', type=int, default=5,
help='number of captions to sample for each image during training. Done for efficiency since CNN forward pass is expensive. E.g. coco has 5 sents/image')
parser.add_argument('--beam_size', type=int, default=1,
help='used when sample_max = 1, indicates number of beams in beam search. Usually 2 or 3 works well. More is not better. Set this to 1 for faster runtime but a bit worse performance.')
# Optimization: for the Language Model
parser.add_argument('--optim', type=str, default='adam',
help='what update to use? rmsprop|sgd|sgdmom|adagrad|adam')
parser.add_argument('--optim_alpha', type=float, default=0.9,
help='alpha for adam')
parser.add_argument('--optim_beta', type=float, default=0.999,
help='beta used for adam')
parser.add_argument('--optim_epsilon', type=float, default=1e-8,
help='epsilon that goes into denominator for smoothing')
parser.add_argument('--weight_decay', type=float, default=0,
help='weight_decay')
parser.add_argument('--label_smoothing', type=float, default=0.2,
help='')
parser.add_argument('--reduce_on_plateau', action='store_true',
help='')
# learning rate
parser.add_argument('--learning_rate', type=float, default=3e-4,
help='learning rate')
parser.add_argument('--learning_rate_decay_start', type=int, default=0,
help='at what epoch to start decaying learning rate? (-1 = dont)')
parser.add_argument('--learning_rate_decay_every', type=int, default=3,
help='every how many iterations thereafter to drop LR?(in epoch)')
parser.add_argument('--learning_rate_decay_rate', type=float, default=0.8,
help='every how many iterations thereafter to drop LR?(in epoch)')
# scheduled sampling
parser.add_argument('--scheduled_sampling_start', type=int, default=0,
help='at what iteration to start decay gt probability')
parser.add_argument('--scheduled_sampling_increase_every', type=int, default=5,
help='every how many iterations thereafter to gt probability')
parser.add_argument('--scheduled_sampling_increase_prob', type=float, default=0.05,
help='How much to update the prob')
parser.add_argument('--scheduled_sampling_max_prob', type=float, default=0.25,
help='Maximum scheduled sampling prob.')
# Evaluation/Checkpointing
parser.add_argument('--val_images_use', type=int, default=5000,
help='how many images to use when periodically evaluating the validation loss? (-1 = all)')
parser.add_argument('--save_checkpoint_every', type=int, default=-1,
help='how often to save a model checkpoint (in iterations)? (-1 = every epoch)')
parser.add_argument('--checkpoint_root', type=str, default='log',
help='root directory to store checkpointed models')
parser.add_argument('--checkpoint_path', type=str, default='',
help='directory to store current checkpoint, \
if not set, it will be assigned as (args.checkpoint_root, args.id) by default. ')
parser.add_argument('--language_eval', type=int, default=1,
help='Evaluate language as well (1 = yes, 0 = no)? BLEU/CIDEr/METEOR/ROUGE_L/SPICE? requires coco-caption code from Github.')
parser.add_argument('--log_loss_every', type=int, default=10,
help='How often do we snapshot losses, for inclusion in the progress dump? (0 = disable)')
# Reward
parser.add_argument('--cider_reward_weight', type=float, default=1,
help='The reward weight from cider')
parser.add_argument('--bleu_reward_weight', type=float, default=0,
help='The reward weight from bleu4')
parser.add_argument('--vrg_data_dir', type=str, default='data/coco_cmb_vrg_final',
help='path to the scene graph data directory, containing numpy files about the '
'labels of object, attribute, and semantic relationships for each image')
parser.add_argument('--rela_gnn_type', type=int, default=0,
help='rela gcn type')
args = parser.parse_args()
if args.checkpoint_path=='':
args.checkpoint_path = os.path.join(args.checkpoint_root, args.id)
if not os.path.exists(args.checkpoint_root):
os.mkdir(args.checkpoint_root)
if not os.path.exists(args.checkpoint_path):
os.mkdir(args.checkpoint_path)
if args.resume_from:
path = os.path.join(args.checkpoint_root, args.resume_from)
assert os.path.exists(path), "%s not exists" %args.resume_from
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
print("[INFO] set CUDA_VISIBLE_DEVICES = %s" % args.gpus)
# Check if args are valid
assert args.rnn_size > 0, "rnn_size should be greater than 0"
assert args.num_layers > 0, "num_layers should be greater than 0"
assert args.input_encoding_size > 0, "input_encoding_size should be greater than 0"
assert args.batch_size > 0, "batch_size should be greater than 0"
assert args.drop_prob_lm >= 0 and args.drop_prob_lm < 1, "drop_prob_lm should be between 0 and 1"
assert args.seq_per_img > 0, "seq_per_img should be greater than 0"
assert args.beam_size > 0, "beam_size should be greater than 0"
assert args.language_eval == 0 or args.language_eval == 1, "language_eval should be 0 or 1"
assert args.load_best_score == 0 or args.load_best_score == 1, "language_eval should be 0 or 1"
assert args.train_only == 0 or args.train_only == 1, "language_eval should be 0 or 1"
return args